Reynolds-averaged Navier–Stokes simulations represent a cost-effective option for practical
engineering applications, but are facing ever-growing demands for more accurate
turbulence models. Recently, emerging machine learning techniques have had a promising
impact on turbulence modeling, but are still in their infancy regarding widespread industrial
adoption. Toward their extensive uptake, this paper presents a universally interpretable
machine learning (UIML) framework for turbulence modeling, which consists of two parallel …